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Generative AI Leader practice exam and study guide

Google Cloud Generative AI Leader

Timed practice exams for Generative AI Leader on the real exam's 90 minute limit. Google states 50 to 60 questions, and a full practice exam on the site serves 60, the top of that range. Every question is original and every option is explained. Your score is broken down by the sections Google Cloud publishes, so you see what to study next. The question bank holds hundreds of practice questions.

Exam facts

  • Questions: 50-60 multiple choice questions [1]
  • Time limit: 90 minutes [1]
  • Level: Foundational [2]
  • Exam fee: $99 (plus tax where applicable) [1]
  • Exam languages (real exam): English, Japanese, Spanish, Portuguese [1]
  • Delivery: Online-proctored or onsite-proctored [1]
  • Validity: 3 years [1]

Tokens: 1 for $4.99, 5 for $14.99, 10 for $24.99 or 25 for $49.99. One token runs a full timed Generative AI Leader practice exam, and every shorter session on that exam is then free. The Unlimited pass is $149.99 for a year of every practice session on every exam.

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Exam sections and weights

Google Cloud publishes these sections and objectives for Generative AI Leader, quoted here as Google Cloud words them. [3]

Section 1: Fundamentals of gen AI, ~30% of the exam

  • "1.1 Describe core generative AI (gen AI) concepts and use cases."
  • "1.2 Describe how various data types are used in gen AI and the business implications."
  • "1.3 Identify the core layers of the gen AI landscape and the business implications."
  • "1.4 Identify the use cases and strengths of Google's foundation models."

Section 2: Google Cloud's gen AI offerings, ~35% of the exam

  • "2.1 Describe Google Cloud's strengths in the field of gen AI."
  • "2.2 Describe how Google Cloud's prebuilt gen AI offerings enable AI powered work."
  • "2.3 Describe how Google Cloud's gen AI offerings improve the customer experience."
  • "2.4 Describe how Google Cloud empowers developers to build with AI."
  • "2.5 Define the purpose and types of tooling for gen AI agents."

Section 3: Techniques to improve gen AI model output, ~20% of the exam

  • "3.1 Describe how to proactively overcome foundation model limitations."
  • "3.2 Describe prompt engineering techniques and how they drive better results."
  • "3.3 Identify grounding techniques and their use cases."

Section 4: Business strategies for a successful gen AI solution, ~15% of the exam

  • "4.1 Describe the Google Cloud-recommended steps to successfully implement a transformational gen AI solution."
  • "4.2 Define secure AI and its importance in protecting AI systems from malicious attacks and misuse."
  • "4.3 Describe the importance of responsible AI in business."

Sample questions

Three questions from the Generative AI Leader practice bank, with the answer and why each option is right or wrong.

Question 1

Before training a demand forecasting model, a logistics company's data team checks its source tables. The warehouse system records one unit weight for each product, and the shipping system records a different weight for the same product. Which data quality dimension does this problem break?

  • A. Completeness
  • B. Uniqueness
  • C. Consistency
  • D. Accuracy

Answer: C. Every rule in an auto data quality scan is tied to a dimension. Consistency covers values that should match across tables or columns but do not, and a model trained on conflicting figures learns conflicting facts. [4]

  • A. Wrong. Nothing required is missing. Both systems hold a weight for every product; the two values disagree.
  • B. Wrong. Nothing here is a repeated record. Each system keeps one weight per product, and those weights conflict.
  • C. Right. Consistency means the same data holds the same values across instances such as tables and columns. A product weight that differs between two systems fails that test.
  • D. Wrong. The team cannot yet say which weight is correct. The check shows two systems that disagree about the same product.

Question 2

An SAP development team writes most of its code in ABAP. It wants to call Gemini models on Gemini Enterprise Agent Platform directly from that code. What option does the team have?

  • A. Agent Platform offers a server SDK for ABAP.
  • B. Only Python can call Agent Platform.
  • C. The team must move its code to Go first.
  • D. ABAP code can reach Gemini only through Google AI Studio.

Answer: A. Broad language support lets teams build with AI in the tools they already use. That lowers the barrier to adoption across an enterprise. [5]

  • A. Right. Gemini Enterprise Agent Platform offers server SDKs for Python, JavaScript and TypeScript, Go, Java, C# and ABAP.
  • B. Wrong. Python is one of several server SDK languages, not the only one.
  • C. Wrong. ABAP has its own server SDK, so no move to another language is needed.
  • D. Wrong. Google AI Studio is a user interface playground, not the way ABAP code calls the platform.

Question 3

A retailer's assistant runs on a large language model with no connection to company data. Customers report that its answers about this quarter's prices and promotions are out of date. Which limitation explains this?

  • A. The model is limited to its pre-trained data.
  • B. The model's context window is too large.
  • C. Safety filters removed the prices.
  • D. The prompts contain too many examples.

Answer: A. A model knows only what it learned in training unless the application supplies more. Retrieval and grounding bring in current, private data. [6]

  • A. Right. LLMs are limited to their pre-trained data, which leads to outdated and potentially inaccurate responses. RAG addresses this by providing up-to-date information.
  • B. Wrong. A larger context window lets the model take in more, but it does not add facts the model never received.
  • C. Wrong. Prices are not harmful content, so safety filters would not remove them.
  • D. Wrong. Examples shape the format of answers. They do not explain missing current facts.

Practice for Generative AI Leader on ExamSens.ai

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References

[1] https://cloud.google.com/learn/certification/generative-ai-leader

[2] https://cloud.google.com/learn/certification

[3] https://services.google.com/fh/files/misc/generative_ai_leader_exam_guide_english.pdf

[4] https://docs.cloud.google.com/knowledge-catalog/docs/auto-data-quality-overview

[5] https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/migrate/migrate-google-ai

[6] https://cloud.google.com/use-cases/retrieval-augmented-generation

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